What data looks like for this category
The data for this category comes from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Daily generated documents include batching records, coating and calendering parameter logs, winding/stacking process records, liquid injection ledgers, formation and grading data, OCV and safety test reports. Data update cycles cover single batch and single cell dimensions. Some parameters such as workshop dew point and coating areal density are updated in real time. A single record includes process number, equipment number, actual measured parameter values, operator identifier, and timestamp.
What constraints these characteristics impose on tool calling and plugin workflows
Multi-source data access requires plugins to integrate with MES systems, dedicated testing equipment APIs, and laboratory data interfaces. Plugins must support different request protocols and return formats. Real-time updated parameters such as workshop dew point and coating areal density need tool calling to trigger at short intervals. Call frequency and timeout thresholds must be configured appropriately. Ledgers generated from high-frequency single-batch and single-cell data require tool calling to filter and aggregate data by process and batch. This avoids importing redundant data. Electrochemical systems have strict data consistency requirements. Tool call return parameters must include complete field validation logic. This prevents abnormal values or empty fields from disrupting subsequent report generation.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts automatic triggering logic for multi-source data, covering ledger data call needs for different processes |
| Tool call timeout duration | 30 seconds | Matches real-time parameter update intervals, prevents process blocking from long waits |
| Maximum number of records returned per call | First 200 records | Matches ledger data volume for single battery batches, avoids context overflow |
| Data aggregation dimension | Group by production batch | Aligns with ledger summary logic for lithium-ion cell manufacturing, facilitates batch-level report generation |
| Workflow/API function-call configuration | Enabled | Forces the large model to return results in the preset tool call format, ensuring accurate parameter parsing |
| Field validation threshold | Calibrated based on actual measurements | Adapts to parameter range requirements for different processes, prevents abnormal data from entering reports |
Three common configuration mistakes
- Phenomenon: MCP service calls fail. Logs show "request timeout" or "response format error". Cause: Timeout thresholds matching real-time parameter update rhythms have not been configured. Or the plugin has not been adapted to non-standard API return formats from dedicated testing equipment.
- Phenomenon: HTTP response input parameter variables cannot be added to the tool call workflow. Cause: Response fields have not been mapped and bound to preset ledger template fields. Or input parameter variable names do not match tool-defined parameter names.
- Phenomenon: The large model does not trigger tool calls. It only generates text reports directly. Cause:
toolChoiceis set tonone. Or trigger conditions for tool calls are not clearly specified in the system prompt.
How to confirm the configuration is correct
- Trigger a tool call. Check if the returned ledger data includes preset category-specific fields such as batching records and coating parameters.
- Review tool call logs. Confirm the timeout duration matches the 30-second configuration. Ensure there are no frequent timeout errors.
- In the FastGPT v4.17.0 workflow editing interface, confirm the tool node is bound to the corresponding data source plugin.
- Test single-batch data aggregation logic. Confirm returned results are displayed grouped by production batch.
The values provided here are common starting points, and should be evaluated against the reader's own samples.